Sensor Network Localization Using Pattern Recognition and Least Squares Kernel Methods
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概要
- 論文の詳細を見る
This paper applies kernel least squares subspace methods to solve the sensor network localization problem. Simulation shows good performance comparable to other studies using kernel methods. The kernel least squares subspace algorithms have low to moderate computational complexity and are very suitable for large dense sensor networks since the scalability of sensor networks is highly desired. Recursive on-line implementation of the kernel algorithms make these algorithms suitable to track locations of mobile sensors.
- 一般社団法人電子情報通信学会の論文
- 2005-05-18
著者
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Kuh Anthony
Department Of Electrical Engineering University Of Hawaii At Manoa
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Zhu Chaopin
Department of Electrical Engineering, University of Hawaii at Manoa
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Zhu Chaopin
Department Of Electrical Engineering University Of Hawaii At Manoa